source/app.py

from __future__ import annotations

import json
from pathlib import Path

import gradio as gr
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import torch
from model import DynamicRoutingCapsuleNet, MatchedMLP
from PIL import Image
from safetensors.torch import load_file

PROJECT_DIR = Path(__file__).resolve().parent
ARTIFACT_DIR = PROJECT_DIR / "artifacts" / "capsule-pocket"
FRAME = pd.read_parquet(PROJECT_DIR / "data" / "test.parquet")
REPORT = json.loads((ARTIFACT_DIR / "evaluation.json").read_text(encoding="utf-8"))
CAPSULE = DynamicRoutingCapsuleNet()
CAPSULE.load_state_dict(load_file(ARTIFACT_DIR / "capsule.safetensors"))
CAPSULE.eval()
MLP = MatchedMLP()
MLP.load_state_dict(load_file(ARTIFACT_DIR / "matched_mlp.safetensors"))
MLP.eval()


@torch.inference_mode()
def inspect_capsules(
    index: int,
    vertical: int,
    horizontal: int,
    occlude: bool,
) -> tuple[Image.Image, go.Figure, dict]:
    row = FRAME.iloc[int(index) % len(FRAME)]
    image = torch.from_numpy(np.asarray(row["image"], dtype=np.float32) / 16).reshape(
        8, 8
    )
    image = torch.roll(image, (int(vertical), int(horizontal)), (0, 1))
    if vertical > 0:
        image[: int(vertical)] = 0
    elif vertical < 0:
        image[int(vertical) :] = 0
    if horizontal > 0:
        image[:, : int(horizontal)] = 0
    elif horizontal < 0:
        image[:, int(horizontal) :] = 0
    if occlude:
        image[3:5, 3:5] = 0
    pixels = image.reshape(1, 64)
    _, lengths = CAPSULE(pixels)
    mlp_logits = MLP(pixels)
    figure = go.Figure(go.Bar(x=list(range(10)), y=lengths[0].numpy()))
    figure.update_layout(
        template="plotly_dark",
        title="Digit-capsule vector lengths",
        xaxis_title="Class",
        yaxis_title="Length",
    )
    rendered = Image.fromarray(
        image.mul(255).to(torch.uint8).numpy(), mode="L"
    ).resize((512, 512), Image.Resampling.NEAREST)
    metrics = {
        "true_label": int(row["label"]),
        "capsule_prediction": int(lengths.argmax(1)),
        "mlp_prediction": int(mlp_logits.argmax(1)),
        "verified_capsule_translation_accuracy": REPORT["results"][
            "dynamic_routing_capsule"
        ]["one_pixel_translation"]["accuracy"],
    }
    return rendered, figure, metrics


with gr.Blocks(title="Capsule Pocket") as demo:
    gr.Markdown(
        "# Capsule Pocket\n"
        "Inspect dynamic-routing capsule lengths beside an exactly parameter-matched "
        "MLP under translation and occlusion."
    )
    with gr.Row():
        index = gr.Slider(0, len(FRAME) - 1, value=8, step=1, label="Test digit")
        vertical = gr.Slider(-1, 1, value=0, step=1, label="Vertical shift")
        horizontal = gr.Slider(-1, 1, value=0, step=1, label="Horizontal shift")
        occlude = gr.Checkbox(False, label="Center occlusion")
    initial = inspect_capsules(8, 0, 0, False)
    with gr.Row():
        image = gr.Image(value=initial[0], label="Input")
        chart = gr.Plot(value=initial[1], label="Capsule lengths")
    metrics = gr.JSON(value=initial[2], label="Matched prediction")
    button = gr.Button("Route capsules", variant="primary")
    button.click(
        inspect_capsules,
        inputs=[index, vertical, horizontal, occlude],
        outputs=[image, chart, metrics],
    )


if __name__ == "__main__":
    demo.launch()